Staff Machine Learning Engineer (Pricing)

Experience designing and deploying real-time model serving (sub-100ms to low-hundreds ms latency targets), including containerization, scalable inference, feature retrieval, and safe rollout strategies (canaries, shadowing, backward-compatible schema evolution).

Strong data engineering fluency: building reliable datasets and features using SQL, Spark/Databricks, and warehouse technologies (e.g., Snowflake), with an understanding of event semantics, identity resolution, and data quality controls.

Working knowledge of experiment design and causal measurement for monetization systems, including pitfalls such as selection bias, interference, and delayed outcomes; familiarity with uplift modeling, bandits, or constrained optimization is a strong plus.

Experience implementing ML monitoring for both technical and business metrics (drift, calibration, segment performance, latency, error budgets) and operating models in production.

Ability to break down ambiguous, high-impact problems, define crisp interfaces and success metrics, and deliver iteratively with strong stakeholder communication.

Strong leadership and mentoring skills and a proven ability to raise the bar on architecture, engineering quality, and operational rigor for ML-powered pricing systems.

Holistic Support

: Enjoy financial assistance for things like hybrid work, family planning, along with generous parental leave, flexible time-off policies, and mental health and wellness resources to support your overall well-being.

Growth Opportunities

: Participate in learning, development, and recognition programs to help you thrive and grow.

Commitment to DEI

: Contribute to diversity, equity, and inclusion through ongoing initiatives and employee resource groups.

Own end-to-end ML systems for pricing optimization, from problem framing and metric definition (e.g., donation yield, conversion, retention, LTV) to model development, launch, and iteration in production.

Design and implement backend model pipelines including feature engineering, training, and evaluation.

Build low-latency real-time inferencing services, including API design, caching strategies, model packaging, and deployment on Kubernetes.

Collaborate with teams to develop instrumentation and event pipelines to capture user and campaign activity required for training and evaluation (e.g., impression/click/submit, donation amount, tip amount, recurring enrollment/cancellation), ensuring schema quality, lineage, and privacy-by-design.

Apply causal and experimental methodologies to measure impact and avoid biased optimization, including online A/B testing design, guardrail metrics, sequential testing considerations, and counterfactual/causal approaches when needed.

Develop optimization approaches appropriate for pricing-like problems, such as uplift modeling, bandits, constrained optimization, calibration, and multi-objective tradeoffs (e.g., yield vs.

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